Curriculum: 60 ECTS
CURRENT TECHNOLOGICAL AND BUSINESS CONTEXT / 4 ECTS (COMPULSORY)
Introduction to the current technological and business context, where Big Data, along with other technological trends (Industry 4.0, the Internet of Things, virtual reality and consultancy augmented reality and IoT consultancy, Blockchain, etc.) will determine the roadmap for Integral Innovation Experts and leadership. This will be the case for many different businesses in the next few years.
DATA SCIENCE / 4 ECTS (COMPULSORY)
Looking in greater depth into the field of Data Science through experiences and real cases explained by professionals who are active in the field through examples and real descriptions.
PROGRAMING LANGUAGES FOR DATA SCIENTISTS / 5 ECTS (COMPULSORY)
Introduction to programing using the main languages and environments that Big Data Scientists need to be familiar with, including R and Python.
STATISTICAL METHODS AND DATA MINING / 9 ECTS
Fonamentació en Estadística, començant des de les bases. Inferència estadística, Anàlisi de Components Principals (ACP), Clusterització i Computació evolutiva.
MACHINE LEARNING / 10 ECTS (COMPULSORY)
Regles d’associació, models lineals, classificadors lineals, SVM, models de sèries temporals, Decision Trees, Random Forest i mètodes de conjunt (ensemble). Processament del llenguatge natural. Xarxes neuronals i Deep Learning. Entorns de Machine Learning en el núvol.
BIG DATA TECHNOLOGIES AND ARCHITECTURES / 4 ECTS (COMPULSORY)
Arquitectura Big Data i Cloud. Ecosistema Hadoop. Bases de dades NoSQL: MongoDB, Neo4J. Paradigma Spark: Spark R, Machine Learning amb Spark MLlib i processament en temps real amb Spark Streaming.
DATA VISUALISATION TECHNIQUES / 4 ECTS (COMPULSORY)
Principles of visualisation. Visualisation with R. Visualisation with Tableau. Visualisation with Open Source tools. Infographs, storytelling with data. Advanced data visualisation techniques.
PROJECTS IN BIG DATA CONTEXTS / 5 ECTS (COMPULSORY)
Data Science Methodologies. Flexible methodologies. Legal context of data. Governing data.
FINAL MASTER'S DEGREE PROJECTS (TFM) / 15 ECTS
The TFM involves developing a real business case in a group. This involves managing the full cycle of the data, including the project design and achievement of value, by means of a product or service, while considering all the necessary aspects for it to be launched onto a real market. The TFM should be handed in as a report and be defended before a jury as a final product.
Curriculum 60 ECTS
- Data analysis needs as a business tool
- New data insight paradigms
- Unstructured data sources. Big data and data analysis
- Presentation of the current ecosystem
- Business models and applications of data analysis
Data Science (5 ECTS) (compulsory)
- Introduction to Data Science
- Difference from traditional statistical analysis
- Data management
- Regulatory environment
- Open data
Languages and Tools for the Data Scientist (5 ECTS) (compulsory)
- Ecosystem and toolkit of the data scientist
- Introduction to R and Python
- Introduction to SQL (Structured Query Language)
Data Analysis: Statistical Methods (10 ECTS) (compulsory)
- Descriptive statistical analysis
- Probability and inference
- Statistical tools: R, Knime, SAS
- Regression models
- Statistical inference
- Statistical hypothesis testing
- R programming language
- Data visualisation using R language
- Statistical inference with R language
- Multivariate statistics
- Principal component analysis
- Connections analysis
- Multidimensional scaling
Data Analysis: Machine Learning (10 ECTS) (compulsory)
- Supervised learning techniques
- Decision trees, Random Forest
- Bayesian models
- Support vector machine classifier
- Neural networks
- Unsupervised learning techniques
- Recommender systems
- Genetic algorithms
- Association rules
- Semantic analysis and natural language processing
Approximate algorithms and stream mining Visualisation (5 ECTS) (compulsory)
- Visualisation and reporting methodologies
- Interactive visualisation
- Visualisation tools
- Practical cases
Big Data Technology and Architecture (3 ECTS) (compulsory)
- Introduction to Big Data technological architecture
- HDFS storage
- Introduction to MapReduce
- Hadoop ecosystem: Flume, Sqoop, Pig, Hive.
- Spark SQL: Processing structured data
- Spark Streaming: Processing in real time
- Spark Mllib: Automatic learning
- NoSQL databases
Project Management in Big Data Contexts (2 ECTS) (compulsory)
- Managing Big Data projects
- Practical cases
Project (15 ECTS). Final Master's Degree Project
The Final Master's Degree Project involves the development of a real business situation in a group, which involves managing the full data life cycle, including the design of the project and the achievement of its value, in terms of a product or service, while taking into account all the necessary aspects for it to be launched onto the market. The Final Master's Degree Project is used to evaluate the competences acquired during the Master’s course, during an academic project that simulates a real company, during which students will be provided with technical and business tutorials by UIC Barcelona and also by the companies involved in the project. The Final Master's Degree Project should be delivered in a project report format and be defended before a jury.
Holder of a degree in computer engineering from the Faculty of IT at BarcelonaTech. Professor of Technology at UIC Barcelona. He combines his academic duties with his position as a Senior IoT Business Consultant at Integral PLM Experts, as well as being the Co-Founder and Director of Business Development at Fantastiq Transmedia, a start-up company that produces mobile technology for the gamification of tours for tourists.
Holder of a degree in computer engineering from the Faculty of IT at BarcelonaTech. Holder of an Executive MBA from EAE Business School. Ms Martín also has a Postgraduate Degree in Big Data Management and Analytics from BarcelonaTech. She has extensive professional experience in positions of responsibility in the fields of IT and Marketing, particularly in digital environments.
Lecturers and professors
- Albert Climent: Arquitectura, Machine Learning, Python
- Carlos Cosials: Data Management, Data Governance, IoT
- Juan de Dios Llamas: Data Research, Data Management, R
- Rodolfo Lomascolo: Arquitectura, Infrastructura, Machine Learning
- Beatriz Martín: Data Management, Digital/Data Strategist
- Pere Millán: Visualización, Sistemas de gráficos
- Imanol Morata: Estadística, Machine Learning, R, Python
- David Roche: Estadística, R, Tableau
- Francisco Rodríguez: Machine Learning, R, Python
The majority of the professors and lecturers are professionals who currently work in companies in this field, who have extensive experience in statistics, analytics, business intelligence and ICT architecture, and innovate in the area of Big Data.
Through their academic capabilities and educational skills, they are key members of the team and act as tutors, personal coaches and academic and professional advisors. All of this ensures that the learning provided in this course reaches a high level.